Knee osteoarthritis (OA) is one of the leading causes of disability among independently living adults. Individuals with knee OA experience pain, stiffness, and decreased range of motion. Despite strong clinical recommendations for rehabilitation that can improve joint function, the exact dosing range has not been found. Studies have shown that both excessive and low amounts of activities and exercise intensity can increase pain, suggesting that there is an optimal, individualized intensity and frequency. The lack of technologies that can collect sufficient biomechanical parameters in the timeline serves as the primary barrier for the development of personalized rehabilitation treatment to cope with dynamically changing functional performances of the joints. Laboratory studies have found that kinetic and kinematic parameters, such as knee adduction moment and flexion angles, can provide objective assessments of physical function and an estimation of OA severity and progression. However, availability of gait laboratories in clinical settings is often lacking and thus provides a severely under-sampled view of the patients? condition. More recently, mobile and wearable technologies have made inroads in clinically oriented research studies. Despite their strong scientific evidence of accuracy and clinical benefits, mobile technologies have not yet been deployed for rehabilitation because of the limitations of current technologies. At present, there exists no engineering solutions that enable long-term ambulatory monitoring of biomechanical parameters and patient behavior that are relevant to knee OA in an efficient, reliable, and accurate manner. This study proposes to develop an integrated mHealth system that can effectively assess the biomechanics of movement associated with OA symptoms and monitor motor activities in free-living conditions based on our recent development of 1) a novel flexible wearable sensor attached to a knee sleeve that measures knee kinematics through an indirect measure of skin stretch and 2) a low-cost, power-efficient instrumented insole that together with the knee sleeve measures knee kinetics. We hypothesize successful completion of this study will generate new knowledge regarding biomechanical OA symptoms and their correlations to the pain, quality of life, and rehabilitation compliance in patients? home and community settings. To accomplish this research goal, Aim 1 will focus on the development of an integrated mHealth system that can collect relevant biomechanical and behavioral data in remote settings, and relay them to the cloud. Aim 2 will evaluate the accuracy and reliability of the wearable system in measuring the relevant biomechanical parameters. Lastly, Aim 3 will explore the robustness and usability of the wearable system in deriving clinically important information in the simulated home environment. This study will yield technological and scientific fundamentals for future studies where we will test our hypothesis that the proposed wearable system can improve rehabilitation adherence by providing objective measures thus improving clinical outcomes, and provide clinicians with important clinical information.